How to Model Agricultural Greenhouse Gas Emissions Using Gro Data
07 May 2019

Understanding agriculture’s role in global greenhouse gas emissions is important to determine the long-term sustainability of the industry as we know it today. Last week, Gro published a Weekly Insight article that described methods the agriculture industry can implement to help mitigate greenhouse gas emissions.

Currently available data on greenhouse gas emissions makes it difficult to track emissions in a timely manner. The data, reported by the UN’s Food and Agriculture Organization (FAO) and other agencies, generally represents annual averages and is published with a lag of about three years. Timely data available in Gro, however, can be used to keep up-to-date on important factors affecting emissions levels and to build predictive models to be used with market-based approaches to curbing carbon pollution.

In order to develop an agricultural emissions tracking model, we would start with FAO data as a baseline. For each country, FAO reports annual values for the following emissions categories: enteric fermentation, manure management, rice cultivation, synthetic fertilizers, manure applied to soils, manure left on pasture, crop residues, cultivation of organic soils, burning of savanna, and burning of crop residues. By identifying data sources that are related to each of these categories, but are released more frequently and with less of a time lag, we can monitor and predict emissions for each category as well as the total emissions amount.

China, India, Brazil, the United States, and Indonesia are the top five polluting countries for agricultural greenhouse gases, but the sources of their emissions vary. In China, nitrogen fertilizer use and rice cultivation are the biggest culprits, while in Indonesia emissions from rice cultivation is the largest single contributor. In India, Brazil, and the US greenhouse gas pollutants stem largely from livestock raising.

Livestock account for 63% of agricultural emissions in the United States. Enteric fermentation and emissions from livestock manure are related to herd size, timely data for which is included in Gro’s data platform. By using that data along with animal methane-conversion ratios, which are available from the US Environmental Protection Agency, livestock emissions can be estimated up to three years in advance of FAO data.

Data contained in Gro on beef cattle herd size, the blue line, along with methane-conversion ratios, can be used to estimate current livestock greenhouse gas emissions. The green line is total US beef cattle emissions, which is only available to 2016 from the FAO.

 

Greenhouse gas emissions from crop planting also can be monitored using Gro’s extensive database. In China, where synthetic fertilizer use has an outsize impact on total emissions, Gro’s province-level area-planted data and pixel-level crop masks can be combined with historical nitrogen fertilizer application rates to generate a local emissions model. Overuse of synthetic nitrogen fertilizers releases nitrous oxide, a potent pollutant.

Emissions produced from rice cultivation and crop residues can be estimated using area planted, area harvested, and yield data to determine how much remaining crop is left decomposing in fields following harvest. Most greenhouse gas emissions from rice cultivation result from the decomposition of organic matter in rice paddies, which produces methane gas.

Finally, the Gro platform also has extensive satellite data that can help identify savanna and other areas where crop residue is being burned.

In China, area planted to corn and fertilizer use have been rising steadily over the last 15 years. These data can be used to estimate current greenhouse gas emissions from synthetic fertilizer applications.

 

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